High-speed gaze detection using a single FPGA for driver assistance systems


The performance of driver gaze detection by video-based eye-tracking often encounters problems in lowcomputing speed, high-power consumption, and installation space constraints inside the vehicle. In this paper, we present an eye-tracking system that uses a single field-programmable-gate-array chip to overcome the aforementioned problems. In the detection system, the image quality is 640 \(\times\) 480 pixels with an 80 fps frame rate. Eye feature extraction is conducted using the enhanced semantics-based vague image representation approach. A succinct fully-connected neural network is then employed to classify various directions of sightline. Our experimental results exhibited a noticeable recognition speed at 0.52 \(\upmu\)s using a 100 MHz system clock and had an average detection rate of 92%.

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Supports from the Ministry of Science and Technology, under Grant MOST106-2221-E-194-060-MY2, Advanced Institute of Manufacturing with High-Tech Innovation (AIM-HI), and Center for Innovative Research on Aging Society (CIRAS) from The Featured Areas Research Center Program within the framework of Higher Education Sprout Project by the Ministry of Education (MoE) in Taiwan are gratefully acknowledged.

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Yu, Y., Ting, Y., Kwok, N. et al. High-speed gaze detection using a single FPGA for driver assistance systems. J Real-Time Image Proc (2020). https://doi.org/10.1007/s11554-020-01004-8

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  • Gaze detection
  • ADAS
  • FPGA
  • FC neural networks